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Record W2239291277 · doi:10.1603/0022-2585-41.4.598

Effect of Aggregation of Horn Fly Populations Within Cattle Herds and Consequences for Sampling to Obtain Unbiased Estimates of Abundance

2004· article· en· W2239291277 on OpenAlexaff
Tim Lysyk, C. D. Steelman

Bibliographic record

VenueJournal of Medical Entomology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHerdBiologyAnimal scienceJackknife resamplingSampling (signal processing)StatisticsVeterinary medicineMathematics

Abstract

fetched live from OpenAlex

Reanalysis of counts of horn fly, Hematobia irritans (L.), obtained from a variety of cattle herds indicated that aggregation of the flies within herds decreased as mean fly density increased. Aggregation was also related to the proportion of fly-resistant and fly-susceptible cattle in a herd. Herds were grouped according to their degree of horn fly aggregation. Low aggregation herds included larger framed Angus, Horned Hereford, Polled Hereford, and Red Poll breeds. Moderate aggregation occurred with Brahman, Charolais, small-framed Angus, mixed cows, and Hereford x Charolais cross. High aggregation occurred with Chianina and mixed herds. Relationships between the sample means and variances varied among aggregation groups. A resampling approach was used to determine the influence of random sampling of a herd on the proportion of horn fly population estimates within fixed percentages of the true mean. The proportion of sample means within +/- 5, 10, 15, and 20% of the true means varied with the proportion of the herd sampled, the mean and variance of fly density, and herd size. Recommendations for obtaining sample size to estimate fly density within a fixed percentage of the true mean are given.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.335
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2004
Admission routes1
Has abstractyes

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